Pipeline run
fc94d0bc-baad-4f45-8a21-495baeb4f809
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
The ideal candidate will be a high-energy, technology and data driven individual who has a track record of running a large global data operations function. • Hire, manage, motivate, and develop a high…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
DataOps Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: dataops-engineer · id: 145 · source: db
Domain=Data Engineering & Analytics; The JD centers on owning global data operations, pipeline observability, monitoring, quality, and operational excellence, which aligns most closely with DataOps Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
R230370 Description About this role Overview We are looking for an innovative hands-on technology leader and run Global Data Operations for one of the largest global FinTech’s. This is a new role that will transform how we manage and process high quality data at scale and reflects our commitment to invest in an Enterprise Data Platform to unlock our data strategy for BlackRock and our Aladdin Client Community. A technology first mindset, to manage and run a modern global data operations function with high levels of automation and engineering, is essential. This role requires a deep understanding of data, domains, and the associated controls. Key Responsibilities The ideal candidate will be a high-energy, technology and data driven individual who has a track record of running a large global data operations function. • Hire, manage, motivate, and develop a highly efficient and diverse global team of data ops and data engineers responsible for moving data through our data pipeline • Ensure on time high quality data delivery with a single pane of glass for data pipeline observability and support • Partner cross-functionally to enhance existing data sets, eliminating manual inputs and ensuring high quality, and onboarding new data sets • Lead change while ensuring daily operational excellence, quality, and control • Build and maintain deep alignment with key internal partners on ops tooling and engineering • Foster an agile collaborative culture which is creative open, supportive, and dynamic Knowledge And Experience • 12+ years’ experience in hands-on data operations including data pipeline monitoring and engineering • Technical expert including experience with data processing, orchestration (Airflow) data ingestion, cloud-based databases/warehousing (Snowflake) and business intelligence tools • The ability to manage and monitor large data sets through the data lifecycle, including the tooling and observability required to be ensure data quality and control at scale • Experience implementing, monitoring, and operating data pipelines that are fast, scalable, reliable, and accurate • Understanding of modern-day data highways, the associated challenges, and effective controls • Passionate about data platforms, data quality and everything data • Practical and detailed oriented operations leader • Inquisitive leader who will bring new ideas that challenge the status quo • Ability to navigate a large, highly matrixed organization • Strong presence with clients • Bachelor’s Degree in Computer Science, Engineering, Mathematics or Statistics Our Benefits To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about. Our hybrid work model BlackRock’s current hybrid work model is designed to enable in-person connections and collaboration that is core to our culture, while supporting increased flexibility for all employees. In line with local health guidance and regulations, employees are required to work at least 3 days in the office each week, with the flexibility to work from home up to 2 days a week. Some business groups may require more time in the office due to their roles and responsibilities. The health, safety and well-being of our people will always be our top priorities; we will continue to monitor local conditions and health advisories in making decisions about our work environments. About BlackRock At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress. This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive. For additional information on BlackRock, please visit careers.blackrock.com | www.blackrock.com/corporate | Instagram: @blackrock | Twitter: @blackrock | LinkedIn: www.linkedin.com/company/blackrock BlackRock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age, disability, family status, gender identity, race, religion, sex, sexual orientation and other protected attributes at law.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Snowflake (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Cloud Platform
- Vendor
- Snowflake Inc.
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 113
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 367
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Airflow | in_db |
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Pipeline Monitoring | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Processing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Ingestion | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Business Intelligence | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Pipeline Observability | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Lifecycle | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "At BlackRock, we are all",
"last_5_words": "and development opportunities to help"
},
"text": "At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children\u2019s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.\n\nThis mission would not be possible without our smartest investment \u2013 the one we make in our employees. It\u2019s why we\u2019re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.",
"word_count": 84
},
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"data platforms",
"Snowflake",
"Agile",
"Make",
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"Observability",
"Airflow",
"Cloud",
"Role",
"roles"
],
"certifications": [],
"company_name": "BlackRock",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Finance"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BACHELOR\u0027S - Computer Science / Engineering / Mathematics / Statistics",
"raw": "Bachelor\u2019s Degree in Computer Science, Engineering, Mathematics or Statistics",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 12,
"raw": "12+ years\u2019 experience in hands-on data operations"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "hybrid"
}
],
"role": "Global Data Operations Leader",
"role_aliases": [
"Data Operations Manager",
"Data Operations Lead",
"Head of Data Operations"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "The ideal candidate will be",
"last_5_words": "creative open, supportive, and dynamic"
},
"text": "The ideal candidate will be a high-energy, technology and data driven individual who has a track record of running a large global data operations function.\n\u2022 Hire, manage, motivate, and develop a highly efficient and diverse global team of data ops and data engineers responsible for moving data through our data pipeline\n\u2022 Ensure on time high quality data delivery with a single pane of glass for data pipeline observability and support\n\u2022 Partner cross-functionally to enhance existing data sets, eliminating manual inputs and ensuring high quality, and onboarding new data sets\n\u2022 Lead change while ensuring daily operational excellence, quality, and control\n\u2022 Build and maintain deep alignment with key internal partners on ops tooling and engineering\n\u2022 Foster an agile collaborative culture which is creative open, supportive, and dynamic",
"word_count": 134
},
{
"bullet_count": 10,
"heading": "Knowledge And Experience",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 12+ years\u2019 experience in",
"last_5_words": "presence with clients"
},
"text": "\u2022 12+ years\u2019 experience in hands-on data operations including data pipeline monitoring and engineering\n\u2022 Technical expert including experience with data processing, orchestration (Airflow) data ingestion, cloud-based databases/warehousing (Snowflake) and business intelligence tools\n\u2022 The ability to manage and monitor large data sets through the data lifecycle, including the tooling and observability required to be ensure data quality and control at scale\n\u2022 Experience implementing, monitoring, and operating data pipelines that are fast, scalable, reliable, and accurate\n\u2022 Understanding of modern-day data highways, the associated challenges, and effective controls\n\u2022 Passionate about data platforms, data quality and everything data\n\u2022 Practical and detailed oriented operations leader\n\u2022 Inquisitive leader who will bring new ideas that challenge the status quo\n\u2022 Ability to navigate a large, highly matrixed organization\n\u2022 Strong presence with clients",
"word_count": 157
}
],
"urls": [
{
"type": "careers",
"url": "https://careers.blackrock.com"
},
{
"type": "website",
"url": "https://www.blackrock.com/corporate"
},
{
"type": "linkedin",
"url": "https://www.linkedin.com/company/blackrock"
},
{
"type": "instagram",
"url": "https://www.instagram.com/blackrock"
},
{
"type": "twitter",
"url": "https://twitter.com/blackrock"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Airflow"
},
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": true,
"skill_name": "Data Pipeline Monitoring"
},
{
"is_primary": true,
"skill_name": "Data Processing"
},
{
"is_primary": true,
"skill_name": "Data Ingestion"
},
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": false,
"skill_name": "Business Intelligence"
},
{
"is_primary": false,
"skill_name": "Data Pipeline Observability"
},
{
"is_primary": true,
"skill_name": "Data Quality"
},
{
"is_primary": false,
"skill_name": "Data Lifecycle"
},
{
"is_primary": true,
"skill_name": "Data Pipelines"
},
{
"is_primary": false,
"skill_name": "Agile"
}
],
"jd_role": {
"display_name": "Global Data Operations Leader",
"rationale": null,
"role_aliases": [
"Data Operations Manager",
"Data Operations Lead",
"Head of Data Operations"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
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},
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"word_count": 84
},
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}
],
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"roles_and_responsibilities": [
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},
{
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},
{
"type": "linkedin",
"url": "https://www.linkedin.com/company/blackrock"
},
{
"type": "instagram",
"url": "https://www.instagram.com/blackrock"
},
{
"type": "twitter",
"url": "https://twitter.com/blackrock"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "fc94d0bc-baad-4f45-8a21-495baeb4f809",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Technical expert including experience with data processing, orchestration (Airflow) data ingestion, cloud-based databases/warehousing (Snowflake) and business intelligence tools",
"similarity": 0.6179
},
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Experience implementing, monitoring, and operating data pipelines that are fast, scalable, reliable, and accurate",
"similarity": 0.5835
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Hire, manage, motivate, and develop a highly efficient and diverse global team of data ops and data engineers responsible for moving data through our data pipeline",
"similarity": 0.5584
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5866,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Build and maintain deep alignment with key internal partners on ops tooling and engineering",
"similarity": 0.5594
},
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Ensure on time high quality data delivery with a single pane of glass for data pipeline observability and support",
"similarity": 0.5568
},
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Experience implementing, monitoring, and operating data pipelines that are fast, scalable, reliable, and accurate",
"similarity": 0.5361
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.5508,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Partner cross-functionally to enhance existing data sets, eliminating manual inputs and ensuring high quality, and onboarding new data sets",
"similarity": 0.5376
},
{
"kra_text": "backend data integration",
"sentence": "Ensure on time high quality data delivery with a single pane of glass for data pipeline observability and support",
"similarity": 0.5082
},
{
"kra_text": "backend data integration",
"sentence": "Technical expert including experience with data processing, orchestration (Airflow) data ingestion, cloud-based databases/warehousing (Snowflake) and business intelligence tools",
"similarity": 0.4517
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API 2 — extract-details
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"relationships": null,
"skill_id": "data-pipelines",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Agile",
"alias_type": "CANONICAL",
"id": 868,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 367,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Agile",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Data Pipeline Monitoring",
"Data Processing",
"Data Ingestion",
"Data Warehousing",
"Business Intelligence",
"Data Pipeline Observability",
"Data Quality",
"Data Lifecycle",
"Data Pipelines"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "DataOps Engineer",
"id": 145,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on owning global data operations, pipeline observability, monitoring, quality, and operational excellence, which aligns most closely with DataOps Engineer.",
"role_archetype": null,
"slug": "dataops-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Airflow",
"tag": "in_db"
},
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "Data Pipeline Monitoring",
"tag": "new"
},
{
"skill": "Data Processing",
"tag": "new"
},
{
"skill": "Data Ingestion",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "Business Intelligence",
"tag": "new"
},
{
"skill": "Data Pipeline Observability",
"tag": "new"
},
{
"skill": "Data Quality",
"tag": "new"
},
{
"skill": "Data Lifecycle",
"tag": "new"
},
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Agile",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 145,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
"id": 54,
"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"dimension_id": 54,
"input_skill": "Airflow",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 265,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 145,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Snowflake",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 105,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 145,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Agile",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 520,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "fc94d0bc-baad-4f45-8a21-495baeb4f809"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.